Musculoskeletal modelers are mindful that the predicted outputs from movement simulations are dependent on inputs that have inherent variability and uncertainty, such as measured kinematics and the parameters that define muscle, tendon, and segment properties. Probabilistic analysis provides quantitative assessment of how sources of variability affect musculoskeletal mechanics, and can provide more confidence to the user when interpreting model outputs. The goal of this project was to develop a generalized, probabilistic plugin for OpenSim and to demonstrate subject-specific and population-based applications of this tool. The tool can implement two probabilistic methods (Monte Carlo and advanced mean value) in a user-friendly graphical user interface to create analyses and visualize results. The probabilistic tool will quantify confidence bounds for output measures and sensitivity factors, which are used to identify the most important input parameters that contribute to output variability. The code is currently written in Matlab but future releases and additions will include applications in Python.